Prologue¶

First of all, A big WELCOME to my dearest teammates:

Ning,
AQ,
Nitin
RottenRotten(腐腐)

It is an honor to meet you all and I am really looking forward to our collaboration on this game.

Back to the topic. The purpose of this notebook is to provide insights of the data sets that we are going to use to in this competition, thus I will explore the dataset in as much details as I can and hopefully by the end of this notebook, I can clear some of your doubts and you have a really good grasp of what we are about to deal with in this game.

A few things to keep in mind before we dive in:

All datasets we need are provided on Kaggle, so we don't need any search for extra datas. You can access them via the following link: https://www.kaggle.com/c/jane-street-market-prediction/overview/evaluation. You can also download them to your own computer if you want to work on them locally.

A github project page was built for us to make plans, post questions and report issues etc. It can be accessed through the link: https://github.com/OssieAckermann/Kaggle/tree/main/Jane-Street-Market-prediction. (No barriers are put, feel free to collaborate)

No modification are made to the datasets in this Notebook. That means no feature selection/computation/training/error anlysis... in this file. The whole point of this file is to provide you with a rather comprehensive view of the dataset.

All images generated by the codes in this Notebook can also be found in the image folder under the same directory

I am actually the one with the lowest degree and the least amount of experience in the team, so if you spot anything that you cannot agree with in this file, it is probably my bad. Please don't hesitate to point it out and question me in the face.

A Brief Introduction of the Four data files we have¶

As the participants of this competition, we are provided with four files, namely:

train.csv - the training set, contains historical data and returns
example_test.csv - a mock test set which represents the structure of the unseen test set. You will not be directly using the test set or sample submission in this competition, as the time-series API will get/set the test set and predictions.
example_sample_submission.csv - a mock sample submission file in the correct format
features.csv - metadata pertaining to the anonymized features

The two files that are marked red are the ones that we need to work with, we will look into them in more details in the later part of this notebook. As for the other two example files, the organizer put them there to give us an idea of what test sets look like. Since in this competition the testing and evaulation will be done by the organizers, we can completely ignor these two files for now.

Import section¶

(This is the section of blocks where I include all the modules that I will need in my codes. Just one little habbit of mine, I feel it is easier to handle your code in this way)

#Import section
import numpy as np
import pandas as pd
import seaborn as sns
import os
import re
import matplotlib.pyplot as plt

import datatable as dt 
# You might not have this package installed on your own computer. 
# But you can simply run pip install command to install it.
# datatable is for loading large size files. Since we have a 5GB data file in this game, it's a good idea to load it
# in dt first and then transform into pandas dataframe.

# Check if you have all the files you need under your working directory
# You should see all four csv files mentioned above
fileNames = os.listdir()
print('File names: ')
for file in fileNames:
    if re.match('[a-zA-Z_]*.csv',file):
        print('  [+]'+file)

File names: 
  [+]example_sample_submission.csv
  [+]train.csv
  [+]example_test.csv
  [+]features.csv

Goal¶

This is the section where we really set our foot in the door.

This competition is evaluated on a utility score.
Each row in the test set represents a trading opportunity for which you will be predicting an action value:

1 --> to make the trade and
0 --> to pass on it.

Each trade j has an associated **weight** and **resp**, which represents a return.

For each date i, we define:

$$P_{i} = {\sum weight_{ij} * resp_{}*action_{ij}}.$$$$ t = {\sum P_{i} \over \sqrt{\sum P_{i}^2}} * \sqrt{250\over|i|}$$

where (|i|) is the number of unique dates in the test set. The utility is then defined as: $$ U = min(max(t, 0), 6)*{\sum p_{i}} $$

Our goal is to maximize the utility score.
In order to do that we need a criterion for determining the value of action.
For example like what most of people did:
- action = 1 when resp (return) > 0
- action = 0 otherwise

Train file info （The 5GB file）¶

This is the dataset that we are going to use to train our model.

Size:
2390491 rows x 138 columns

Columns:

Feature_i ( i = 0,1,...129) * Each trade characterized by 130 anonymous features.
Data: the date where trade happened. We are given 500 days of trading days(2 years)
resp: return, in accordance to the description
resp_i: (i = 1,2,3,4) return over different time periods. Serves as supplymentary datas.
weight: weight on each trade ( Note. They do not add up to 1 for some unknown reason)
ts_id: trading ID, same as the index of this dataframe

Unfortunately, all the features are anonymous meaning there is no way to know extractly what they are. Although we can make some deductions or presumptions on these features based on their distributions, null value patterns and tag structure, I am afraid that finance hardly plays a role in this competition.

Anyway, let's take a look into the dataset.

# Load in files
df_train = dt.fread('train.csv')
df_train = df_train.to_pandas()

# Head view
df_train.head()

#Tail view
df_train.tail()

Missing values¶

Now that we have viewed what the data are like, the next thing we care about is whether or not the dataset contains missing value for obvious reasons: defective data are not helpful at all in training our models.

And this leads to our next question: how do we handle the missing values? There are many different ways to handle missing data. Here I will list three most commom ways:

Simply remove the rows that has missing value in it
Replace the null value with 0
Replace the null value with some large value e.g. 999

For now, I will just stop here and leave this question to our discussion.
Now, Let's take a close look at those null value.

# Calculating how many null/missing values in each columns
pd.DataFrame(df_train.isnull().sum())

# Same as above except in percentage this time
cm = sns.light_palette("red", as_cmap=True)
pd.DataFrame(df_train.isnull().sum()/2390491).style.background_gradient(cmap=cm)

Note that some features have severe issue with missing values (the red ones). On top of that, it's even more interesting to discover that same number of missing values are found across different features (e.g 7 vs 8; 17 vs 18; 27 vs 28 )

Now let's filter out these features (severe ones) and see what we can find.

null_percentage = df_train.isnull().sum()/2390491 
null_percentage_mask = null_percentage > 0.1
filter_features = pd.DataFrame(null_percentage[null_percentage_mask],columns = ['Null value percentage'])
filter_features

# Features with more than 10% missing value
filter_features.index

Index(['feature_7', 'feature_8', 'feature_17', 'feature_18', 'feature_27',
       'feature_28', 'feature_72', 'feature_78', 'feature_84', 'feature_90',
       'feature_96', 'feature_102', 'feature_108', 'feature_114'],
      dtype='object')

df_train[list(filter_features.index)]

Return¶

In the training set, train.csv, you are provided a resp value, as well as several other resp_{1,2,3,4} values that represent returns over different time horizons.

And as you will see, as i --> larger; resp_i --> less volatility, and return tends to be stable during 100th - 200th day

#Head view of returns
df_train[['resp','resp_1','resp_2','resp_3']].head()

fig, ax1 = plt.subplots(1,1,figsize = (15,8))
df_train_daily = df_train[['date','resp', 'resp_1','resp_2','resp_3','resp_4']].groupby('date').sum()

sns.lineplot(x = df_train_daily.index, y = df_train_daily['resp'], ax = ax1, color = 'red', label = 'resp')
sns.lineplot(x = df_train_daily.index, y = df_train_daily['resp_1'], ax = ax1, color = 'blue', label = 'resp_1')
sns.lineplot(x = df_train_daily.index, y = df_train_daily['resp_2'], ax = ax1, color = 'green', label = 'resp_2')
sns.lineplot(x = df_train_daily.index, y = df_train_daily['resp_3'], ax = ax1, color = 'orange', label = 'resp_3')


plt.title('Daily return', fontsize = 20)

Text(0.5, 1.0, 'Daily return')

fig, ax1 = plt.subplots(1,1,figsize = (15,8))

sns.lineplot(x = df_train_daily.index, y = df_train_daily['resp'].cumsum(), ax = ax1, color = 'red',label = 'resp')
sns.lineplot(x = df_train_daily.index, y = df_train_daily['resp_1'].cumsum(), ax = ax1, color = 'blue', label = 'resp_1')
sns.lineplot(x = df_train_daily.index, y = df_train_daily['resp_2'].cumsum(), ax = ax1, color = 'green', label = 'resp_2')
sns.lineplot(x = df_train_daily.index, y = df_train_daily['resp_3'].cumsum(), ax = ax1, color = 'purple', label = 'resp_3')

plt.title('Cumulative returns', fontsize = 20)

Text(0.5, 1.0, 'Cumulative returns')

Features¶

In my opinion, the key to win this game = right features + right model
Feature reduction/selection is an important in this competition.

We will need to discuss which features to drop.

In this section, I will show some visualizations of features' distributions and correlations. While some of their distributions have interesting shapes while the correlations are rather equal among all the features. Again, it proves the need of features processing.

# Plotting (Do not run this, it takes long time)
fig, ax = plt.subplots(13,10, figsize = (52,40))
for i in range(13):
    for j in range(10):
        index = 10*i+j
        sns.distplot(df_train['feature_'+str(index)], ax = ax[i,j])
# visualization of distributions of features
fig.tight_layout(pad=2.0)
fig

# Visualization of the correlation matrix
cm = sns.diverging_palette(5, 250, as_cmap=True)
mask_ut=np.triu(np.ones(df_train.shape)).astype(np.bool)
df_train_corr = df_train.corr()
df_train_corr.style.background_gradient(cmap=cm, axis=None,).set_precision(2)

# Features correlation with respect to resp only
pd.DataFrame(df_train_corr['resp']).sort_values('resp',ascending = False).style.background_gradient(cmap='YlGnBu')

Informations on daily returns and daily volumes¶

Just in case you also happended to be interested in the volumes Note that the return was relatively stable for a short while between 100 and 200 for some unknown reasons.

#Visualization: Daily Return vs Daily Volume

Volume = df_train.date
Volume = Volume.value_counts().sort_index()

daily_return = df_train[['date','resp']].groupby(['date']).sum()
daily_return

plt.rcParams["figure.figsize"] = (30,8)

ax_return = plt.subplot2grid((5,4),(0,0),rowspan = 3,colspan = 4)
ax_volumes = plt.subplot2grid((5,4),(3,0),colspan = 4,rowspan = 2)
ax_return.plot(daily_return.index, daily_return.resp, label = "Return")
ax_return.set_title("Return and Volume",fontsize = 30)
ax_return.legend(fontsize = 20)
ax_return.set_xlabel('Time',fontsize = 20)
ax_return.set_ylabel('Return',fontsize = 20)
ax_return.tick_params(axis = 'both',labelsize = 16)

plt.subplots_adjust(wspace = 0.5,hspace = 0.5)
ax_volumes.set_ylabel('Volume',fontsize = 20)
ax_volumes.bar(Volume.index, Volume, color = 'red',alpha = 0.7)
ax_volumes.tick_params(axis = 'both',labelsize = 16)

daily_return.describe()

pd.DataFrame(Volume.describe())

Feature file info¶

Other competitioners are not talking about / using this file that much on Kaggle.

Each feature is consist of 28 tags.
No information in regards of tags are given.
Some features are suspected to be duplicates since they have exactly the same tags structure. However, those features don't hold the same values or distributions, therefore tags are not primary keys to features(meaning we can't distinguish features by just looking at their tags.)

df_feature = pd.read_csv('features.csv')

df_feature.head()

#Duplicated features (in terms of tags)
feature_dup = df_feature.drop(['feature'], axis = 1).duplicated(subset=None, keep='first')
feature_dup[feature_dup == True]

42    True
43    True
48    True
50    True
51    True
52    True
61    True
63    True
65    True
66    True
67    True
68    True
dtype: bool

# Features that has same tags
duplicated = df_feature.loc[feature_dup[feature_dup == True].index]
duplicated

Summary¶

Again, the goal of this game is to maximize the utility score, in order to do that we need to make sure that we have right features to train the right model. At this point, the problems we need to tackle/discuss are:

1. Decide action value. Are we simply go with action = 1 when resp > 0 and action = 0 otherwise or is there any other thoughts that you guys may have?

2. How do we handle the missing values in this case? Drop them/assign 0 / assign 999/ others?

3. Most importantly, how do we select our features?

A couple of my points:

I think it is important to remember that this competition will last till August. Just because our models have nice performance at present doesn't mean our models will have nice outputs in the future. It could simply be overfitting.

Since we do not know what these features are and these features are in different scales, we should normalize them before feed them to models.

I don't think it is a good idea to simply remove all the null values as we have seen that null value percentage can take up to more than 15% for some features.

	weight	resp_1	resp_2	resp_3	resp_4	resp	feature_0	feature_1	feature_2	...	feature_121	feature_122	feature_123	feature_124	feature_125	feature_126	feature_127	feature_128	feature_129	ts_id
0	0.000000	0.009916	0.014079	0.008773	0.001390	0.006270	1	-1.872746	-2.191242	...	NaN	1.168391	8.313583	1.782433	14.018213	2.653056	12.600292	2.301488	11.445807	0
1	16.673515	-0.002828	-0.003226	-0.007319	-0.011114	-0.009792	-1	-1.349537	-1.704709	...	NaN	-1.178850	1.777472	-0.915458	2.831612	-1.417010	2.297459	-1.304614	1.898684	1
2	0.000000	0.025134	0.027607	0.033406	0.034380	0.023970	-1	0.812780	-0.256156	...	NaN	6.115747	9.667908	5.542871	11.671595	7.281757	10.060014	6.638248	9.427299	2
3	0.000000	-0.004730	-0.003273	-0.000461	-0.000476	-0.003200	-1	1.174378	0.344640	...	NaN	2.838853	0.499251	3.033732	1.513488	4.397532	1.266037	3.856384	1.013469	3
4	0.138531	0.001252	0.002165	-0.001215	-0.006219	-0.002604	1	-3.172026	-3.093182	...	NaN	0.344850	4.101145	0.614252	6.623456	0.800129	5.233243	0.362636	3.926633	4

	date	weight	resp_1	resp_2	resp_3	resp_4	resp	feature_0	feature_1	feature_2	...	feature_121	feature_122	feature_123	feature_124	feature_125	feature_126	feature_127	feature_128	feature_129	ts_id
2390486	499	0.000000	0.000142	0.000142	0.005829	0.020342	0.015396	1	-1.649365	-1.169996	...	-1.896874	-1.260055	1.947725	-1.994399	-1.685163	-2.866165	-0.216130	-1.892048	0.901585	2390486
2390487	499	0.000000	0.000012	0.000012	-0.000935	-0.006326	-0.004718	1	2.432943	5.284504	...	-0.936553	1.064936	3.119762	-0.419796	-0.208975	-0.146749	0.730166	0.648452	2.068737	2390487
2390488	499	0.000000	0.000499	0.000499	0.007605	0.024907	0.016591	1	-0.622475	-0.963682	...	-2.956745	-0.640334	-2.279663	-0.950259	-4.388417	-1.669922	-3.288939	-1.336142	-2.814239	2390488
2390489	499	0.283405	-0.000156	-0.000156	-0.001375	-0.003702	-0.002004	-1	-1.463757	-1.107228	...	-2.035894	-1.780962	0.881246	-2.202140	-1.912601	-3.341684	-0.571188	-2.185795	0.627452	2390489
2390490	499	0.000000	-0.001855	-0.001855	-0.001194	-0.000864	-0.001905	-1	-1.817184	-1.131577	...	-0.571013	2.483421	8.284037	-0.698486	0.199953	-0.168395	2.051091	1.726072	5.823676	2390490

	0
date	0.000000
weight	0.000000
resp_1	0.000000
resp_2	0.000000
resp_3	0.000000
resp_4	0.000000
resp	0.000000
feature_0	0.000000
feature_1	0.000000
feature_2	0.000000
feature_3	0.000187
feature_4	0.000187
feature_5	0.000000
feature_6	0.000000
feature_7	0.164458
feature_8	0.164458
feature_9	0.000330
feature_10	0.000330
feature_11	0.033472
feature_12	0.033472
feature_13	0.006423
feature_14	0.006423
feature_15	0.002796
feature_16	0.002796
feature_17	0.165462
feature_18	0.165462
feature_19	0.000330
feature_20	0.000330
feature_21	0.034070
feature_22	0.034070
feature_23	0.006423
feature_24	0.006423
feature_25	0.002796
feature_26	0.002796
feature_27	0.165462
feature_28	0.165462
feature_29	0.000330
feature_30	0.000330
feature_31	0.034070
feature_32	0.034070
feature_33	0.006423
feature_34	0.006423
feature_35	0.002796
feature_36	0.002796
feature_37	0.000000
feature_38	0.000000
feature_39	0.000000
feature_40	0.000000
feature_41	0.000000
feature_42	0.000000
feature_43	0.000000
feature_44	0.000187
feature_45	0.000187
feature_46	0.000000
feature_47	0.000000
feature_48	0.000000
feature_49	0.000000
feature_50	0.000000
feature_51	0.000000
feature_52	0.000000
feature_53	0.000000
feature_54	0.000000
feature_55	0.028617
feature_56	0.000301
feature_57	0.000000
feature_58	0.000000
feature_59	0.000020
feature_60	0.000000
feature_61	0.000000
feature_62	0.000000
feature_63	0.000000
feature_64	0.000000
feature_65	0.000000
feature_66	0.000000
feature_67	0.000000
feature_68	0.000000
feature_69	0.000000
feature_70	0.000000
feature_71	0.000000
feature_72	0.147010
feature_73	0.000330
feature_74	0.026810
feature_75	0.006423
feature_76	0.002796
feature_77	0.000000
feature_78	0.147010
feature_79	0.000330
feature_80	0.026810
feature_81	0.006423
feature_82	0.002796
feature_83	0.000000
feature_84	0.147010
feature_85	0.000330
feature_86	0.026810
feature_87	0.006423
feature_88	0.002796
feature_89	0.000000
feature_90	0.147010
feature_91	0.000330
feature_92	0.026810
feature_93	0.006423
feature_94	0.002796
feature_95	0.000000
feature_96	0.147010
feature_97	0.000330
feature_98	0.026810
feature_99	0.006423
feature_100	0.002796
feature_101	0.000000
feature_102	0.147010
feature_103	0.000330
feature_104	0.026810
feature_105	0.006423
feature_106	0.002796
feature_107	0.000000
feature_108	0.147010
feature_109	0.000330
feature_110	0.026810
feature_111	0.006423
feature_112	0.002796
feature_113	0.000000
feature_114	0.147010
feature_115	0.000330
feature_116	0.026810
feature_117	0.006423
feature_118	0.002796
feature_119	0.000000
feature_120	0.029222
feature_121	0.029222
feature_122	0.000093
feature_123	0.000093
feature_124	0.006728
feature_125	0.006728
feature_126	0.003703
feature_127	0.003703
feature_128	0.000804
feature_129	0.000804
ts_id	0.000000

	feature_7	feature_8	feature_17	feature_18	feature_27	feature_28	feature_72	feature_78	feature_84	feature_90	feature_96	feature_102	feature_108	feature_114
0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2390486	0.526201	1.162469	0.928914	2.150500	-0.041265	-0.149770	-0.539591	-2.627014	-0.193006	1.689489	0.104432	2.754382	-0.128550	2.107612
2390487	-0.064645	0.163244	0.170509	0.526144	-1.301215	-3.244589	1.752626	1.352782	2.578997	0.893971	2.297062	0.614411	3.295566	0.719010
2390488	0.140248	0.039213	0.437936	0.443755	-0.013002	-0.004482	0.213130	0.316766	0.206049	-0.073920	0.108439	-0.248634	0.217940	-0.174350
2390489	-0.683812	-0.939522	-0.968833	-1.735649	0.026421	0.049269	-0.523474	-3.177544	0.875750	6.022596	1.157290	7.345104	1.492829	6.529618
2390490	0.198607	0.752480	-0.163660	-0.477443	0.601387	1.804146	0.525496	0.580039	0.025297	-0.658249	-0.372650	-1.277911	-0.298838	-0.927916

	resp	resp_1	resp_2	resp_3
0	0.006270	0.009916	0.014079	0.008773
1	-0.009792	-0.002828	-0.003226	-0.007319
2	0.023970	0.025134	0.027607	0.033406
3	-0.003200	-0.004730	-0.003273	-0.000461
4	-0.002604	0.001252	0.002165	-0.001215

	resp
resp	1.000000
resp_4	0.956197
resp_3	0.815972
resp_2	0.595274
resp_1	0.452159
feature_27	0.048293
feature_31	0.047021
feature_39	0.043932
feature_33	0.041301
feature_35	0.033564
feature_28	0.032502
feature_29	0.030466
feature_32	0.029504
feature_40	0.028748
feature_34	0.023818
feature_7	0.021207
feature_53	0.021034
feature_11	0.020725
feature_122	0.020313
feature_128	0.019902
feature_1	0.019802
feature_126	0.019669
feature_124	0.019478
feature_13	0.019412
feature_36	0.019326
feature_120	0.018552
feature_69	0.018534
feature_9	0.018368
feature_42	0.018267
feature_15	0.018183
feature_30	0.017356
feature_43	0.015688
feature_8	0.014660
feature_101	0.014392
feature_113	0.013843
feature_44	0.013619
feature_96	0.013469
feature_89	0.013273
feature_108	0.013209
feature_12	0.012481
feature_79	0.011976
feature_2	0.011883
feature_84	0.011500
feature_14	0.010752
feature_73	0.010445
feature_71	0.010297
feature_10	0.010188
feature_85	0.009896
feature_91	0.009741
feature_16	0.009508
feature_82	0.008672
feature_110	0.008643
feature_98	0.008454
feature_88	0.007700
feature_76	0.007584
feature_56	0.007423
feature_86	0.007228
feature_59	0.006887
feature_55	0.006565
feature_58	0.006430
feature_57	0.005959
feature_107	0.005761
feature_119	0.005590
feature_87	0.005486
feature_111	0.005089
feature_47	0.004869
feature_5	0.004814
feature_94	0.004809
feature_54	0.004621
feature_102	0.004613
feature_95	0.004548
feature_114	0.004385
feature_81	0.003926
feature_112	0.003753
feature_3	0.003608
feature_90	0.003604
feature_75	0.002835
feature_99	0.002824
feature_63	0.002754
feature_70	0.002682
feature_62	0.002421
feature_116	0.001978
feature_123	0.001857
feature_109	0.001766
feature_6	0.001706
feature_104	0.001637
feature_92	0.001140
feature_60	0.001021
feature_129	0.000985
feature_93	0.000954
feature_61	0.000943
feature_4	0.000500
feature_45	0.000288
feature_127	0.000084
feature_78	0.000072
feature_80	0.000002
feature_64	-0.000387
feature_117	-0.000406
feature_74	-0.000408
feature_46	-0.000490
feature_67	-0.000993
feature_68	-0.001200
feature_125	-0.001211
feature_48	-0.001442
feature_66	-0.001547
feature_121	-0.001682
feature_65	-0.001783
feature_118	-0.001882
feature_72	-0.002034
feature_105	-0.002306
feature_100	-0.002728
feature_115	-0.002743
feature_49	-0.004349
feature_83	-0.006124
feature_20	-0.006614
feature_50	-0.006891
weight	-0.006948
ts_id	-0.007526
date	-0.007590
feature_106	-0.007671
feature_77	-0.008028
feature_97	-0.008253
feature_52	-0.008280
feature_41	-0.008511
feature_26	-0.009957
feature_51	-0.010348
feature_103	-0.010920
feature_19	-0.012886
feature_24	-0.015145
feature_22	-0.016168
feature_18	-0.017448
feature_25	-0.018248
feature_38	-0.020683
feature_0	-0.023726
feature_23	-0.024656
feature_21	-0.027753
feature_17	-0.028277
feature_37	-0.029281

	resp
count	500.000000
mean	1.952129
std	11.085616
min	-48.649796
25%	-2.616761
50%	1.187239
75%	5.285396
max	57.957487

	feature	tag_0	tag_1	tag_2	tag_3	tag_4	tag_5	tag_6	tag_7	tag_8	...	tag_19	tag_20	tag_21	tag_22	tag_23	tag_24	tag_25	tag_26	tag_27	tag_28
0	feature_0	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
1	feature_1	False	False	False	False	False	False	True	True	False	...	False	False	False	False	False	False	False	False	False	False
2	feature_2	False	False	False	False	False	False	True	True	False	...	False	False	False	False	False	False	False	False	False	False
3	feature_3	False	False	False	False	False	False	True	False	True	...	False	False	False	False	False	False	False	False	False	False
4	feature_4	False	False	False	False	False	False	True	False	True	...	False	False	False	False	False	False	False	False	False	False

	date	weight	resp_1	resp_2	resp_3	resp_4	resp	feature_0	feature_1	feature_2	feature_3	feature_4	feature_5	feature_6	feature_7	feature_8	feature_9	feature_10	feature_11	feature_12	feature_13	feature_14	feature_15	feature_16	feature_17	feature_18	feature_19	feature_20	feature_21	feature_22	feature_23	feature_24	feature_25	feature_26	feature_27	feature_28	feature_29	feature_30	feature_31	feature_32	feature_33	feature_34	feature_35	feature_36	feature_37	feature_38	feature_39	feature_40	feature_41	feature_42	feature_43	feature_44	feature_45	feature_46	feature_47	feature_48	feature_49	feature_50	feature_51	feature_52	feature_53	feature_54	feature_55	feature_56	feature_57	feature_58	feature_59	feature_60	feature_61	feature_62	feature_63	feature_64	feature_65	feature_66	feature_67	feature_68	feature_69	feature_70	feature_71	feature_72	feature_73	feature_74	feature_75	feature_76	feature_77	feature_78	feature_79	feature_80	feature_81	feature_82	feature_83	feature_84	feature_85	feature_86	feature_87	feature_88	feature_89	feature_90	feature_91	feature_92	feature_93	feature_94	feature_95	feature_96	feature_97	feature_98	feature_99	feature_100	feature_101	feature_102	feature_103	feature_104	feature_105	feature_106	feature_107	feature_108	feature_109	feature_110	feature_111	feature_112	feature_113	feature_114	feature_115	feature_116	feature_117	feature_118	feature_119	feature_120	feature_121	feature_122	feature_123	feature_124	feature_125	feature_126	feature_127	feature_128	feature_129	ts_id
date	1.00	0.03	-0.01	-0.01	-0.01	-0.01	-0.01	0.00	-0.03	0.02	-0.00	-0.00	-0.01	-0.01	-0.01	-0.00	-0.05	0.00	-0.02	0.00	-0.03	0.00	-0.04	0.01	-0.01	0.00	-0.02	-0.01	-0.01	-0.00	-0.02	-0.00	-0.02	-0.01	-0.01	-0.00	-0.03	-0.01	-0.01	-0.00	-0.02	-0.01	-0.02	-0.01	-0.00	-0.00	-0.00	-0.00	0.03	-0.03	-0.13	-0.08	-0.14	-0.05	-0.06	-0.02	0.01	-0.05	0.01	-0.01	-0.06	-0.04	-0.09	-0.06	-0.08	-0.08	-0.07	-0.02	-0.02	-0.01	-0.01	-0.04	-0.02	-0.02	-0.04	-0.04	-0.03	-0.01	0.02	0.00	-0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.01	-0.01	0.01	-0.00	0.00	0.01	-0.03	-0.09	-0.03	-0.06	-0.05	-0.03	-0.10	-0.01	0.02	-0.00	0.01	0.01	-0.03	-0.09	-0.02	-0.06	-0.05	-0.03	-0.10	-0.02	0.03	-0.00	0.01	0.02	-0.03	-0.09	-0.04	-0.07	-0.06	-0.05	-0.10	-0.06	-0.04	-0.05	-0.04	-0.06	-0.04	-0.05	-0.04	-0.05	-0.04	1.00
weight	0.03	1.00	-0.02	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	0.06	-0.00	-0.00	-0.00	-0.00	-0.02	-0.00	-0.06	0.02	-0.03	0.00	-0.04	0.01	-0.05	0.02	-0.01	-0.00	-0.03	-0.01	-0.01	-0.00	-0.01	0.00	-0.02	-0.00	-0.01	0.00	-0.03	-0.00	-0.01	0.01	-0.01	0.01	-0.02	-0.00	-0.00	-0.00	-0.00	0.00	0.34	-0.21	-0.17	-0.01	0.06	0.08	0.06	0.07	0.08	0.22	0.54	0.28	-0.18	0.02	-0.00	-0.01	0.00	0.00	0.00	0.01	0.01	0.00	-0.00	0.02	0.02	0.02	0.02	0.02	-0.01	0.25	0.07	0.00	0.00	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.01	-0.04	-0.04	-0.04	-0.04	-0.04	-0.03	0.05	0.03	0.04	0.04	0.03	0.07	-0.04	-0.04	-0.04	-0.04	-0.04	-0.03	0.06	0.02	0.04	0.03	0.02	0.07	-0.05	-0.05	-0.05	-0.05	-0.06	-0.03	0.05	0.06	0.05	0.05	0.05	0.06	-0.21	-0.02	-0.20	-0.01	-0.21	-0.02	-0.22	-0.01	-0.21	-0.01	0.03
resp_1	-0.01	-0.02	1.00	0.89	0.68	0.36	0.45	-0.01	0.05	0.04	0.02	0.02	0.02	0.02	0.03	0.03	0.04	0.03	0.04	0.03	0.04	0.02	0.04	0.03	0.00	0.01	0.02	0.02	0.00	0.00	0.01	0.01	0.01	0.01	0.04	0.03	0.03	0.02	0.04	0.02	0.03	0.02	0.03	0.02	-0.00	0.00	0.03	0.02	-0.01	0.02	0.02	0.01	0.00	-0.01	-0.00	-0.01	-0.01	-0.01	-0.02	-0.02	0.04	0.02	0.00	0.00	0.00	0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	-0.01	-0.00	-0.01	-0.01	0.05	0.03	0.04	-0.00	-0.00	-0.00	-0.00	-0.00	-0.01	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.01	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.00	-0.00	0.00	-0.01	0.01	0.01	0.01	0.01	0.01	0.00	0.00	0.00	0.00	0.00	0.00	-0.00	0.01	0.01	0.01	0.01	0.01	0.00	0.00	0.00	0.00	0.00	0.00	-0.00	0.03	0.01	0.04	0.02	0.03	0.01	0.03	0.01	0.04	0.01	-0.01
resp_2	-0.01	-0.01	0.89	1.00	0.82	0.47	0.60	-0.01	0.04	0.03	0.02	0.02	0.02	0.02	0.03	0.03	0.04	0.03	0.04	0.02	0.03	0.02	0.03	0.02	-0.01	0.00	0.00	0.01	-0.01	-0.00	-0.00	-0.00	-0.00	0.00	0.04	0.03	0.03	0.02	0.04	0.03	0.04	0.02	0.03	0.02	-0.01	-0.00	0.03	0.02	-0.01	0.02	0.02	0.01	0.00	-0.01	-0.00	-0.01	-0.01	-0.01	-0.02	-0.02	0.04	0.02	0.00	0.01	0.00	0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.01	-0.01	0.04	0.02	0.03	-0.00	0.00	-0.00	-0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	-0.00	0.01	0.01	0.01	0.00	0.00	0.00	0.00	0.00	0.00	-0.00	0.00	-0.00	0.01	0.00	0.01	0.01	0.00	0.01	0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.01	0.01	0.01	0.01	0.01	0.01	0.00	0.00	0.00	-0.00	0.00	-0.00	0.03	0.01	0.03	0.01	0.03	0.01	0.03	0.01	0.03	0.01	-0.01
resp_3	-0.01	-0.01	0.68	0.82	1.00	0.81	0.82	-0.00	0.03	0.02	-0.00	-0.00	0.02	0.01	0.02	0.01	0.03	0.02	0.02	0.02	0.02	0.01	0.02	0.01	-0.00	0.00	0.01	0.01	-0.00	-0.00	-0.00	0.00	0.01	0.01	0.02	0.02	0.01	0.01	0.03	0.02	0.02	0.01	0.01	0.01	-0.01	-0.00	0.01	0.01	-0.01	0.02	0.01	0.01	0.00	-0.00	0.00	-0.00	-0.01	-0.00	-0.01	-0.01	0.03	0.01	0.01	0.01	0.01	0.01	0.01	-0.00	-0.00	0.00	0.00	-0.00	0.00	-0.00	-0.01	-0.01	0.03	0.01	0.02	-0.00	-0.00	-0.00	-0.00	-0.00	-0.01	-0.00	-0.00	-0.00	-0.00	-0.00	-0.01	0.01	0.00	0.00	-0.00	-0.00	0.01	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.01	0.00	0.01	0.01	0.01	0.01	0.00	-0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.01	0.00	0.00	0.01	0.00	-0.00	0.00	-0.00	-0.00	0.00	0.02	0.00	0.02	0.01	0.02	0.00	0.02	0.00	0.02	0.01	-0.01
resp_4	-0.01	-0.01	0.36	0.47	0.81	1.00	0.96	-0.01	0.02	0.01	-0.01	-0.01	0.00	0.00	0.02	0.01	0.02	0.01	0.02	0.01	0.02	0.01	0.02	0.01	-0.02	-0.01	-0.00	-0.00	-0.02	-0.01	-0.02	-0.01	-0.01	-0.00	0.03	0.02	0.02	0.01	0.03	0.02	0.03	0.02	0.02	0.01	-0.02	-0.01	0.02	0.02	-0.01	0.02	0.01	0.01	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.01	-0.01	0.02	0.00	0.01	0.01	0.01	0.01	0.01	-0.00	-0.00	0.00	0.00	-0.00	0.00	0.00	-0.00	-0.00	0.01	0.00	0.01	-0.00	0.00	-0.00	-0.00	0.00	-0.01	-0.00	0.01	-0.00	0.00	0.00	-0.01	0.01	0.00	0.01	0.00	0.00	0.01	0.00	0.00	-0.00	-0.00	0.00	0.00	0.01	-0.00	0.01	0.00	0.00	0.01	0.01	-0.01	0.00	-0.00	-0.00	0.01	0.01	0.00	0.01	0.00	0.00	0.01	0.00	-0.00	0.00	-0.00	-0.00	0.01	0.02	-0.00	0.02	0.00	0.02	-0.00	0.02	-0.00	0.02	0.00	-0.01
resp	-0.01	-0.01	0.45	0.60	0.82	0.96	1.00	-0.02	0.02	0.01	0.00	0.00	0.00	0.00	0.02	0.01	0.02	0.01	0.02	0.01	0.02	0.01	0.02	0.01	-0.03	-0.02	-0.01	-0.01	-0.03	-0.02	-0.02	-0.02	-0.02	-0.01	0.05	0.03	0.03	0.02	0.05	0.03	0.04	0.02	0.03	0.02	-0.03	-0.02	0.04	0.03	-0.01	0.02	0.02	0.01	0.00	-0.00	0.00	-0.00	-0.00	-0.01	-0.01	-0.01	0.02	0.00	0.01	0.01	0.01	0.01	0.01	0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.02	0.00	0.01	-0.00	0.01	-0.00	0.00	0.01	-0.01	0.00	0.01	0.00	0.00	0.01	-0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.00	0.01	0.00	0.00	0.00	0.00	0.01	-0.01	0.01	0.00	-0.00	0.01	0.00	-0.01	0.00	-0.00	-0.01	0.01	0.01	0.00	0.01	0.01	0.00	0.01	0.00	-0.00	0.00	-0.00	-0.00	0.01	0.02	-0.00	0.02	0.00	0.02	-0.00	0.02	0.00	0.02	0.00	-0.01
feature_0	0.00	-0.01	-0.01	-0.01	-0.00	-0.01	-0.02	1.00	0.02	0.02	-0.02	-0.01	-0.01	0.03	-0.02	-0.01	0.03	0.03	-0.01	-0.00	0.02	0.02	0.03	0.03	0.66	0.67	0.69	0.73	0.71	0.63	0.70	0.71	0.71	0.73	-0.65	-0.67	-0.71	-0.74	-0.66	-0.69	-0.69	-0.67	-0.73	-0.73	0.72	0.74	-0.71	-0.75	-0.01	0.01	0.01	-0.01	-0.00	0.02	0.02	0.02	0.01	0.02	0.00	-0.01	0.02	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.01	-0.01	0.01	0.01	-0.00	0.03	0.02	-0.04	-0.04	0.03	0.02	0.02	-0.12	-0.58	-0.22	-0.37	-0.53	-0.02	-0.13	-0.52	-0.20	-0.35	-0.45	-0.02	-0.04	-0.51	-0.10	-0.29	-0.45	-0.01	-0.05	-0.52	-0.11	-0.22	-0.36	-0.01	0.06	0.52	0.15	0.23	0.41	-0.00	0.06	0.44	0.11	0.22	0.41	0.00	0.01	0.02	0.01	0.01	0.02	-0.00	0.01	0.02	0.01	0.01	0.02	-0.00	0.01	0.00	0.02	0.00	0.01	-0.00	0.01	0.00	0.02	0.00	0.00
feature_1	-0.03	-0.01	0.05	0.04	0.03	0.02	0.02	0.02	1.00	0.86	0.01	0.01	0.00	-0.00	0.12	0.08	0.72	0.58	0.22	0.16	0.39	0.28	0.62	0.49	0.12	0.08	0.27	0.22	0.15	0.12	0.20	0.16	0.24	0.20	0.06	0.04	0.21	0.16	0.10	0.08	0.15	0.12	0.17	0.15	0.05	0.03	0.00	-0.00	-0.32	0.44	0.33	0.02	-0.17	0.23	0.47	0.30	0.17	0.08	-0.01	-0.15	0.46	0.07	0.07	0.06	0.07	0.07	0.06	0.02	0.02	0.03	0.03	0.00	-0.02	-0.02	-0.02	-0.02	0.91	0.54	0.75	-0.01	-0.04	-0.01	-0.03	-0.04	-0.03	-0.00	-0.04	-0.01	-0.02	-0.03	-0.01	0.09	0.16	0.11	0.13	0.14	0.08	-0.12	0.04	-0.08	-0.04	0.01	-0.14	0.11	0.21	0.13	0.16	0.20	0.08	-0.12	0.10	-0.06	-0.02	0.05	-0.13	0.13	0.39	0.15	0.21	0.34	0.09	-0.12	0.17	-0.07	-0.01	0.10	-0.13	0.42	-0.04	0.43	-0.02	0.43	-0.03	0.44	-0.03	0.44	-0.03	-0.03
feature_2	0.02	0.06	0.04	0.03	0.02	0.01	0.01	0.02	0.86	1.00	0.01	0.01	-0.01	-0.01	0.08	0.09	0.55	0.67	0.15	0.19	0.29	0.32	0.47	0.56	0.08	0.09	0.21	0.25	0.11	0.13	0.15	0.17	0.18	0.22	0.04	0.05	0.17	0.20	0.08	0.09	0.11	0.14	0.14	0.18	0.03	0.03	0.00	0.01	-0.23	0.15	-0.09	0.00	-0.19	0.11	0.22	0.20	0.18	0.07	0.08	-0.14	0.24	0.15	0.05	0.05	0.05	0.05	0.04	0.01	0.01	0.02	0.02	-0.01	-0.02	-0.02	-0.03	-0.04	0.75	0.70	0.89	-0.00	-0.03	-0.01	-0.02	-0.03	0.01	0.00	-0.03	-0.00	-0.02	-0.02	0.02	0.07	0.18	0.10	0.15	0.17	0.02	-0.16	0.04	-0.10	-0.05	0.01	-0.21	0.08	0.22	0.12	0.16	0.21	0.01	-0.16	0.08	-0.09	-0.03	0.04	-0.21	0.10	0.42	0.14	0.22	0.37	0.02	-0.16	0.16	-0.10	-0.03	0.08	-0.20	0.18	0.05	0.20	0.08	0.18	0.06	0.20	0.07	0.20	0.07	0.02
feature_3	-0.00	-0.00	0.02	0.02	-0.00	-0.01	0.00	-0.02	0.01	0.01	1.00	0.91	0.70	0.59	0.37	0.34	0.05	0.05	0.24	0.21	0.14	0.13	0.08	0.07	0.12	0.12	0.01	0.02	0.05	0.07	0.02	0.04	0.01	0.02	0.21	0.17	0.05	0.03	0.14	0.12	0.10	0.08	0.06	0.04	0.42	0.41	0.46	0.42	-0.01	0.03	0.02	-0.02	-0.02	0.02	0.02	0.02	0.01	0.00	0.00	-0.03	0.05	0.02	0.00	0.01	0.00	0.00	0.00	0.02	0.02	0.03	0.03	0.02	-0.01	-0.00	-0.03	-0.02	0.04	0.02	0.03	-0.01	0.01	-0.01	-0.01	0.00	-0.01	-0.01	0.01	-0.01	-0.00	0.00	-0.01	-0.02	0.04	-0.00	0.01	0.03	-0.02	-0.02	0.03	-0.01	0.00	0.03	-0.02	-0.01	0.03	0.00	0.02	0.03	-0.01	-0.01	0.02	-0.00	0.01	0.02	-0.01	-0.01	0.06	0.00	0.02	0.05	-0.01	-0.01	0.05	-0.00	0.02	0.04	-0.01	0.03	-0.00	0.04	0.01	0.04	0.00	0.04	0.01	0.04	0.01	-0.00
feature_4	-0.00	-0.00	0.02	0.02	-0.00	-0.01	0.00	-0.01	0.01	0.01	0.91	1.00	0.63	0.65	0.36	0.40	0.04	0.05	0.23	0.26	0.14	0.16	0.07	0.08	0.13	0.16	0.02	0.03	0.07	0.10	0.04	0.06	0.02	0.03	0.18	0.19	0.03	0.02	0.12	0.12	0.08	0.08	0.04	0.04	0.40	0.46	0.41	0.45	-0.01	0.01	-0.00	-0.02	-0.01	0.01	0.01	0.01	0.01	0.00	0.01	-0.02	0.03	0.03	0.00	0.01	0.00	0.00	0.01	0.02	0.02	0.03	0.03	0.02	-0.01	-0.01	-0.02	-0.02	0.03	0.02	0.04	-0.01	-0.00	-0.01	-0.01	-0.00	-0.01	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.01	0.03	0.00	0.01	0.03	-0.01	-0.01	0.03	-0.00	0.01	0.03	-0.02	-0.01	0.03	0.01	0.02	0.03	-0.01	-0.01	0.03	0.01	0.02	0.03	-0.01	-0.00	0.06	0.01	0.03	0.05	-0.01	-0.01	0.05	0.01	0.03	0.04	-0.01	0.01	0.00	0.02	0.02	0.02	0.01	0.02	0.01	0.02	0.02	-0.00
feature_5	-0.01	-0.00	0.02	0.02	0.02	0.00	0.00	-0.01	0.00	-0.01	0.70	0.63	1.00	0.91	0.28	0.25	0.04	0.03	0.18	0.16	0.11	0.09	0.06	0.04	0.08	0.11	0.01	0.04	0.03	0.07	0.01	0.04	0.00	0.03	0.18	0.12	0.04	-0.00	0.12	0.07	0.08	0.04	0.05	0.01	0.29	0.32	0.35	0.29	-0.01	0.03	0.02	-0.02	-0.02	-0.00	0.02	0.01	0.00	-0.01	-0.01	-0.03	0.04	0.02	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.02	0.02	0.02	-0.01	-0.00	-0.02	-0.02	0.03	0.01	0.02	-0.01	0.00	-0.01	-0.01	-0.00	-0.01	-0.01	0.00	-0.01	-0.01	-0.00	-0.00	-0.02	0.03	-0.01	-0.00	0.02	-0.02	-0.02	0.03	-0.01	-0.00	0.02	-0.02	-0.01	0.02	0.00	0.01	0.03	-0.01	-0.02	0.02	-0.00	0.01	0.02	-0.02	-0.01	0.04	-0.00	0.02	0.04	-0.02	-0.02	0.04	-0.00	0.01	0.03	-0.02	0.03	-0.00	0.04	0.01	0.03	0.00	0.03	0.00	0.03	0.01	-0.01
feature_6	-0.01	-0.00	0.02	0.02	0.01	0.00	0.00	0.03	-0.00	-0.01	0.59	0.65	0.91	1.00	0.25	0.28	0.03	0.03	0.16	0.17	0.09	0.10	0.04	0.04	0.11	0.15	0.03	0.06	0.07	0.11	0.04	0.08	0.03	0.06	0.12	0.10	0.00	-0.03	0.07	0.05	0.04	0.02	0.01	-0.02	0.29	0.36	0.26	0.25	-0.00	0.01	0.01	-0.02	-0.02	-0.00	0.01	0.00	-0.00	-0.01	-0.00	-0.02	0.03	0.02	0.01	0.01	0.01	0.01	0.01	0.02	0.02	0.02	0.02	0.02	-0.01	-0.00	-0.01	-0.01	0.02	0.01	0.02	-0.01	-0.01	-0.01	-0.02	-0.02	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	-0.00	-0.02	0.01	-0.00	-0.00	0.01	-0.02	-0.02	0.01	-0.01	0.00	0.01	-0.02	-0.01	0.04	0.01	0.02	0.04	-0.01	-0.01	0.03	0.00	0.02	0.03	-0.01	-0.01	0.04	0.00	0.02	0.04	-0.01	-0.01	0.04	0.00	0.02	0.03	-0.01	0.02	-0.00	0.02	0.01	0.02	0.00	0.02	0.01	0.02	0.01	-0.01
feature_7	-0.01	-0.02	0.03	0.03	0.02	0.02	0.02	-0.02	0.12	0.08	0.37	0.36	0.28	0.25	1.00	0.90	0.20	0.16	0.59	0.53	0.38	0.33	0.24	0.20	0.40	0.37	0.05	0.05	0.18	0.20	0.10	0.10	0.05	0.05	0.49	0.43	0.10	0.07	0.29	0.25	0.19	0.16	0.11	0.09	0.23	0.23	0.28	0.26	-0.07	0.12	0.09	-0.02	-0.05	0.05	0.10	0.06	0.03	0.00	-0.02	-0.05	0.16	0.06	0.01	0.02	0.01	0.01	0.01	0.02	0.02	0.03	0.03	0.01	-0.01	-0.01	-0.03	-0.03	0.15	0.05	0.11	-0.01	0.01	-0.00	-0.00	0.00	-0.02	-0.01	0.01	-0.00	-0.00	0.00	-0.01	-0.02	0.06	0.00	0.02	0.05	-0.01	-0.05	0.04	-0.03	-0.01	0.02	-0.05	-0.01	0.05	0.01	0.03	0.04	-0.00	-0.05	0.03	-0.03	-0.00	0.01	-0.05	-0.00	0.11	0.02	0.05	0.10	-0.01	-0.05	0.06	-0.02	0.01	0.04	-0.05	0.13	-0.00	0.14	0.03	0.14	0.01	0.14	0.02	0.14	0.03	-0.01
feature_8	-0.00	-0.00	0.03	0.03	0.01	0.01	0.01	-0.01	0.08	0.09	0.34	0.40	0.25	0.28	0.90	1.00	0.14	0.18	0.53	0.60	0.33	0.38	0.19	0.22	0.37	0.43	0.04	0.05	0.18	0.24	0.09	0.12	0.04	0.07	0.42	0.46	0.07	0.07	0.26	0.27	0.16	0.18	0.09	0.10	0.21	0.25	0.24	0.27	-0.05	0.04	-0.01	-0.01	-0.04	0.02	0.04	0.03	0.03	0.00	0.01	-0.04	0.09	0.08	0.01	0.02	0.01	0.01	0.01	0.02	0.02	0.03	0.03	0.02	-0.02	-0.01	-0.03	-0.03	0.11	0.08	0.13	-0.00	0.01	-0.00	-0.00	0.00	-0.01	0.00	0.00	0.00	-0.00	0.00	0.00	-0.00	0.06	0.02	0.04	0.06	-0.01	-0.04	0.03	-0.01	0.01	0.03	-0.05	0.00	0.05	0.02	0.04	0.05	-0.01	-0.04	0.03	-0.01	0.01	0.02	-0.05	0.01	0.11	0.03	0.06	0.10	-0.01	-0.03	0.06	-0.00	0.02	0.05	-0.05	0.06	0.02	0.08	0.06	0.06	0.04	0.07	0.05	0.07	0.05	-0.00
feature_9	-0.05	-0.06	0.04	0.04	0.03	0.02	0.02	0.03	0.72	0.55	0.05	0.04	0.04	0.03	0.20	0.14	1.00	0.83	0.34	0.26	0.57	0.42	0.86	0.70	0.18	0.14	0.39	0.31	0.24	0.19	0.30	0.24	0.35	0.29	0.10	0.08	0.28	0.22	0.16	0.13	0.22	0.18	0.24	0.21	0.09	0.07	0.02	0.02	-0.39	0.57	0.42	0.00	-0.20	0.24	0.53	0.32	0.16	0.06	-0.06	-0.16	0.67	0.19	0.07	0.05	0.07	0.07	0.05	0.02	0.02	0.02	0.03	-0.02	-0.04	-0.04	-0.05	-0.05	0.77	0.31	0.58	-0.01	-0.05	-0.02	-0.03	-0.04	-0.04	-0.01	-0.04	-0.01	-0.02	-0.04	-0.02	0.08	0.15	0.10	0.12	0.13	0.06	-0.17	0.01	-0.12	-0.08	-0.01	-0.19	0.10	0.21	0.13	0.16	0.20	0.07	-0.17	0.08	-0.10	-0.04	0.03	-0.18	0.12	0.40	0.14	0.21	0.35	0.07	-0.16	0.15	-0.11	-0.04	0.07	-0.18	0.57	-0.02	0.61	0.05	0.59	-0.00	0.61	0.01	0.61	0.03	-0.05
feature_10	0.00	0.02	0.03	0.03	0.02	0.01	0.01	0.03	0.58	0.67	0.05	0.05	0.03	0.03	0.16	0.18	0.83	1.00	0.27	0.32	0.47	0.51	0.71	0.84	0.14	0.16	0.32	0.37	0.19	0.22	0.25	0.27	0.30	0.34	0.08	0.10	0.24	0.28	0.13	0.16	0.19	0.22	0.21	0.27	0.07	0.08	0.02	0.03	-0.28	0.23	-0.04	-0.01	-0.21	0.11	0.25	0.21	0.18	0.05	0.04	-0.16	0.41	0.30	0.04	0.05	0.05	0.05	0.04	-0.00	-0.00	0.02	0.02	-0.03	-0.05	-0.04	-0.06	-0.06	0.61	0.48	0.72	-0.00	-0.04	-0.01	-0.03	-0.04	0.00	0.00	-0.03	-0.01	-0.02	-0.03	0.02	0.06	0.19	0.10	0.15	0.17	-0.01	-0.20	0.03	-0.13	-0.07	0.00	-0.26	0.07	0.23	0.13	0.17	0.22	-0.01	-0.21	0.08	-0.11	-0.05	0.03	-0.26	0.10	0.45	0.14	0.23	0.40	-0.00	-0.20	0.15	-0.13	-0.04	0.08	-0.25	0.28	0.08	0.34	0.17	0.30	0.11	0.32	0.13	0.34	0.15	0.01
feature_11	-0.02	-0.03	0.04	0.04	0.02	0.02	0.02	-0.01	0.22	0.15	0.24	0.23	0.18	0.16	0.59	0.53	0.34	0.27	1.00	0.90	0.64	0.56	0.41	0.34	0.35	0.32	0.12	0.10	0.38	0.39	0.23	0.21	0.13	0.12	0.38	0.34	0.12	0.09	0.45	0.39	0.26	0.24	0.14	0.12	0.17	0.17	0.18	0.17	-0.13	0.21	0.15	-0.02	-0.08	0.08	0.18	0.11	0.05	0.01	-0.02	-0.07	0.25	0.08	0.02	0.03	0.02	0.02	0.02	0.02	0.02	0.03	0.03	0.01	-0.02	-0.01	-0.03	-0.03	0.26	0.09	0.19	-0.01	-0.01	-0.01	-0.01	-0.01	-0.02	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01	0.00	0.07	0.01	0.03	0.06	0.00	-0.07	0.03	-0.05	-0.03	0.01	-0.07	0.01	0.08	0.02	0.05	0.08	0.01	-0.07	0.04	-0.04	-0.01	0.02	-0.07	0.02	0.16	0.04	0.08	0.14	0.01	-0.07	0.08	-0.04	-0.00	0.05	-0.07	0.21	-0.01	0.23	0.04	0.22	0.00	0.22	0.02	0.23	0.03	-0.02
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feature_13	-0.03	-0.04	0.04	0.03	0.02	0.02	0.02	0.02	0.39	0.29	0.14	0.14	0.11	0.09	0.38	0.33	0.57	0.47	0.64	0.57	1.00	0.88	0.67	0.57	0.28	0.25	0.23	0.19	0.36	0.34	0.40	0.36	0.25	0.22	0.24	0.21	0.17	0.13	0.35	0.30	0.37	0.34	0.19	0.17	0.14	0.13	0.11	0.10	-0.23	0.34	0.24	-0.01	-0.13	0.14	0.31	0.19	0.10	0.03	-0.04	-0.11	0.40	0.12	0.04	0.03	0.04	0.04	0.03	0.02	0.02	0.03	0.03	0.00	-0.03	-0.02	-0.04	-0.03	0.45	0.17	0.33	-0.01	-0.03	-0.01	-0.03	-0.03	-0.03	-0.01	-0.03	-0.01	-0.02	-0.02	-0.01	0.03	0.10	0.04	0.05	0.09	0.02	-0.11	0.03	-0.08	-0.05	0.01	-0.12	0.04	0.14	0.06	0.09	0.13	0.03	-0.11	0.07	-0.06	-0.02	0.03	-0.11	0.06	0.26	0.07	0.13	0.23	0.03	-0.11	0.12	-0.07	-0.01	0.07	-0.11	0.35	-0.02	0.37	0.04	0.36	-0.00	0.36	0.01	0.37	0.03	-0.03
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